Inference through innovation processes tested in the authorship attribution task

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985807%3A_____%2F24%3A00599642" target="_blank" >RIV/67985807:_____/24:00599642 - isvavai.cz</a>
Result on the web
<a href="https://doi.org/10.1038/s42005-024-01714-6" target="_blank" >https://doi.org/10.1038/s42005-024-01714-6</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1038/s42005-024-01714-6" target="_blank" >10.1038/s42005-024-01714-6</a>

Result language
angličtina
Original language name
Inference through innovation processes tested in the authorship attribution task
Original language description
Urn models for innovation capture fundamental empirical laws shared by several real-world processes. The so-called urn model with triggering includes, as particular cases, the urn representation of the two-parameter Poisson-Dirichlet process and the Dirichlet process, seminal in Bayesian non-parametric inference. In this work, we leverage this connection to introduce a general approach for quantifying closeness between symbolic sequences and test it within the framework of the authorship attribution problem. The method demonstrates high accuracy when compared to other related methods in different scenarios, featuring a substantial gain in computational efficiency and theoretical transparency. Beyond the practical convenience, this work demonstrates how the recently established connection between urn models and non-parametric Bayesian inference can pave the way for designing more efficient inference methods. In particular, the hybrid approach that we propose allows us to relax the exchangeability hypothesis, which can be particularly relevant for systems exhibiting complex correlation patterns and non-stationary dynamics.
Czech name
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Czech description
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Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
—
OECD FORD branch
10103 - Statistics and probability

Project
<a href="/en/project/GA21-17211S" target="_blank" >GA21-17211S: Network modelling of complex systems: from correlation graphs to information hypergraphs</a><br>
Continuities
I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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